Memory Depth, Not Memory Access: Selective Parametric Consolidation for Long-Running Language Agents
arXiv:2606.26806v1 Announce Type: cross Abstract: Long-running language agents need more than memory access. Retrieval systems can fetch past facts at query time, but they do not decide which experiences should continue to shape behavior after the working context is unloaded. We study this separate problem as memory depth: durable goal-conditioned tendencies written into a small parametric store. We introduce the loop-drift protocol, a controlled stress test in which the retrieval index remains intact while working context is unloaded and goal-conditioned behavior must persist under long-loop
The increasing complexity and continuous operation of AI agents demand more sophisticated memory management beyond simple retrieval, pushing current research towards parametric consolidation.
Improving memory depth in language agents is critical for their long-term efficacy, adaptability, and ability to learn complex behaviors without constant re-training or losing past experience.
The focus in AI agent development shifts from mere memory access to deeply integrated, parametric memory consolidation, enabling more robust and continuously evolving autonomous systems.
- · AI agent developers
- · Companies deploying long-running AI agents
- · AI compute infrastructure providers
- · Companies relying solely on basic retrieval-augmented generation architectures
- · AI models with shallow memory capabilities
AI agents will exhibit more consistent, goal-driven behavior over extended periods, reducing drift and improving reliability.
This improved agent capability will accelerate the automation of complex, multi-step workflows across various industries, impacting white-collar labor.
The enhanced learning and adaptation of these agents could lead to emergent behaviors and intelligence, potentially accelerating the development of truly autonomous systems that operate independently for prolonged durations.
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Read at arXiv cs.LG